For the nominal-the-best problem, use the quadratic loss function L(y, t) = c(y − t) 2 to measure the quality loss of deviation of the response y from the target t. If there is an adjustment factor,...



For the nominal-the-best problem, use the quadratic loss function L(y, t) =


c(y − t)


2 to measure the quality loss of deviation of the response y from the


target t. If there is an adjustment factor, use the two-step procedure to choose


optimal factor settings:


(i) Select levels of some factors to minimize Var(y).


(ii) Select the level of a factor not used in (i) to move E(y) closer to t.


An adjustment factor is a factor that has a significant effect on the mean but


not on the variance. See Section 4.10. To carry out this procedure in the data


analysis, use linear regression to model the sample average ȳ and the log sample variance ln s2 in terms of the factor effects. See the analysis in Section 4.12


for an illustration.



May 26, 2022
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